In [132]:
cm = ConfusionMatrixDisplay(confusion_matrix = confusion_matrix(y_test, y_predict), display_labels = zoo_class["Class_Type"].unique())
cm.plot(xticks_rotation = 45)
plt.show()
#import scikit's Random Forest library
from sklearn.ensemble import RandomForestClassifier
#Loading pandas
import pandas as pd
#loading numpy
import numpy as np
#loading tree visualizations
import matplotlib.pyplot as plt
#Loads model performance
from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay, classification_report
#Creating an object for zoo dataset
zoo = pd.read_csv('zoo.csv')
#Creating an object for zoo class dataset
zoo_class = pd.read_csv('class.csv')
#Printng out the dataset info
print(zoo)
print(zoo_class)
animal_name hair feathers eggs milk airborne aquatic predator \
0 aardvark 1 0 0 1 0 0 1
1 antelope 1 0 0 1 0 0 0
2 bass 0 0 1 0 0 1 1
3 bear 1 0 0 1 0 0 1
4 boar 1 0 0 1 0 0 1
.. ... ... ... ... ... ... ... ...
96 wallaby 1 0 0 1 0 0 0
97 wasp 1 0 1 0 1 0 0
98 wolf 1 0 0 1 0 0 1
99 worm 0 0 1 0 0 0 0
100 wren 0 1 1 0 1 0 0
toothed backbone breathes venomous fins legs tail domestic \
0 1 1 1 0 0 4 0 0
1 1 1 1 0 0 4 1 0
2 1 1 0 0 1 0 1 0
3 1 1 1 0 0 4 0 0
4 1 1 1 0 0 4 1 0
.. ... ... ... ... ... ... ... ...
96 1 1 1 0 0 2 1 0
97 0 0 1 1 0 6 0 0
98 1 1 1 0 0 4 1 0
99 0 0 1 0 0 0 0 0
100 0 1 1 0 0 2 1 0
catsize class_type
0 1 1
1 1 1
2 0 4
3 1 1
4 1 1
.. ... ...
96 1 1
97 0 6
98 1 1
99 0 7
100 0 2
[101 rows x 18 columns]
Class_Number Number_Of_Animal_Species_In_Class Class_Type \
0 1 41 Mammal
1 2 20 Bird
2 3 5 Reptile
3 4 13 Fish
4 5 4 Amphibian
5 6 8 Bug
6 7 10 Invertebrate
Animal_Names
0 aardvark, antelope, bear, boar, buffalo, calf,...
1 chicken, crow, dove, duck, flamingo, gull, haw...
2 pitviper, seasnake, slowworm, tortoise, tuatara
3 bass, carp, catfish, chub, dogfish, haddock, h...
4 frog, frog, newt, toad
5 flea, gnat, honeybee, housefly, ladybird, moth...
6 clam, crab, crayfish, lobster, octopus, scorpi...
#Viewing the top 10 rows of the dataset
zoo.head(10)
| animal_name | hair | feathers | eggs | milk | airborne | aquatic | predator | toothed | backbone | breathes | venomous | fins | legs | tail | domestic | catsize | class_type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | aardvark | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 4 | 0 | 0 | 1 | 1 |
| 1 | antelope | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 4 | 1 | 0 | 1 | 1 |
| 2 | bass | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 4 |
| 3 | bear | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 4 | 0 | 0 | 1 | 1 |
| 4 | boar | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 4 | 1 | 0 | 1 | 1 |
| 5 | buffalo | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 4 | 1 | 0 | 1 | 1 |
| 6 | calf | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 4 | 1 | 1 | 1 | 1 |
| 7 | carp | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 4 |
| 8 | catfish | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 4 |
| 9 | cavy | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 4 | 0 | 1 | 0 | 1 |
#Viewing the
zoo_class.head(10)
| Class_Number | Number_Of_Animal_Species_In_Class | Class_Type | Animal_Names | |
|---|---|---|---|---|
| 0 | 1 | 41 | Mammal | aardvark, antelope, bear, boar, buffalo, calf,... |
| 1 | 2 | 20 | Bird | chicken, crow, dove, duck, flamingo, gull, haw... |
| 2 | 3 | 5 | Reptile | pitviper, seasnake, slowworm, tortoise, tuatara |
| 3 | 4 | 13 | Fish | bass, carp, catfish, chub, dogfish, haddock, h... |
| 4 | 5 | 4 | Amphibian | frog, frog, newt, toad |
| 5 | 6 | 8 | Bug | flea, gnat, honeybee, housefly, ladybird, moth... |
| 6 | 7 | 10 | Invertebrate | clam, crab, crayfish, lobster, octopus, scorpi... |
print(zoo['class_type'])
0 1
1 1
2 4
3 1
4 1
..
96 1
97 6
98 1
99 7
100 2
Name: class_type, Length: 101, dtype: int64
zoo_class["Class_Number"].unique()
array([1, 2, 3, 4, 5, 6, 7], dtype=int64)
zoo_class["Class_Type"].unique()
array(['Mammal', 'Bird', 'Reptile', 'Fish', 'Amphibian', 'Bug',
'Invertebrate'], dtype=object)
zoo['species'] = pd.Categorical.from_codes(zoo.class_type, categories= ['None','Mammal', 'Bird', 'Reptile', 'Fish', 'Amphibian', 'Bug',
'Invertebrate'])
zoo.head(10)
| animal_name | hair | feathers | eggs | milk | airborne | aquatic | predator | toothed | backbone | breathes | venomous | fins | legs | tail | domestic | catsize | class_type | species | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | aardvark | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 4 | 0 | 0 | 1 | 1 | Mammal |
| 1 | antelope | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 4 | 1 | 0 | 1 | 1 | Mammal |
| 2 | bass | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 4 | Fish |
| 3 | bear | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 4 | 0 | 0 | 1 | 1 | Mammal |
| 4 | boar | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 4 | 1 | 0 | 1 | 1 | Mammal |
| 5 | buffalo | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 4 | 1 | 0 | 1 | 1 | Mammal |
| 6 | calf | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 4 | 1 | 1 | 1 | 1 | Mammal |
| 7 | carp | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 4 | Fish |
| 8 | catfish | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 4 | Fish |
| 9 | cavy | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 4 | 0 | 1 | 0 | 1 | Mammal |
#loading train and testing utility functions
from sklearn.model_selection import train_test_split
x = zoo.iloc[:, 1:17]
y = zoo.iloc[:, 17]
#Splitting dataset into a trainig dataset and test dataset
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 0)
rf = RandomForestClassifier(n_estimators = 10, random_state = 0)
rf.fit(x_train, y_train)
rf.estimators_
[DecisionTreeClassifier(max_features='sqrt', random_state=209652396), DecisionTreeClassifier(max_features='sqrt', random_state=398764591), DecisionTreeClassifier(max_features='sqrt', random_state=924231285), DecisionTreeClassifier(max_features='sqrt', random_state=1478610112), DecisionTreeClassifier(max_features='sqrt', random_state=441365315), DecisionTreeClassifier(max_features='sqrt', random_state=1537364731), DecisionTreeClassifier(max_features='sqrt', random_state=192771779), DecisionTreeClassifier(max_features='sqrt', random_state=1491434855), DecisionTreeClassifier(max_features='sqrt', random_state=1819583497), DecisionTreeClassifier(max_features='sqrt', random_state=530702035)]
y_predict = rf.predict(x_test)
accuracy = accuracy_score(y_test, y_predict)
print("The accuracy of test data: ", accuracy)
The accuracy of test data: 1.0
cm = ConfusionMatrixDisplay(confusion_matrix = confusion_matrix(y_test, y_predict), display_labels = zoo_class["Class_Type"].unique())
cm.plot(xticks_rotation = 45)
plt.show()
#Creating a list of the feature column names
features = zoo.columns[1:17]
#Viewing features
features
Index(['hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator',
'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail',
'domestic', 'catsize'],
dtype='object')
target = zoo_class["Class_Type"].unique()
target
array(['Mammal', 'Bird', 'Reptile', 'Fish', 'Amphibian', 'Bug',
'Invertebrate'], dtype=object)
from sklearn.tree import plot_tree
for i in range(len(rf.estimators_)):
plt.figure("Decison Tree", figsize = [20,15])
plot_tree(rf.estimators_[i], fontsize = 10, filled = True, feature_names = features, class_names = target)
plt.tight_layout()
plt.show()
